Abstract
We address the problem of Duplicate Question Detection (DQD) in low-resource domain-specific Community Question Answering forums. Our multi-view framework MV-DASE combines an ensemble of sentence encoders via Generalized Canonical Correlation Analysis, using unlabeled data only. In our experiments, the ensemble includes generic and domain-specific averaged word embeddings, domain-finetuned BERT and the Universal Sentence Encoder. We evaluate MV-DASE on the CQADupStack corpus and on additional low-resource Stack Exchange forums. Combining the strengths of different encoders, we significantly outperform BM25, all single-view systems as well as a recent supervised domain-adversarial DQD method.- Anthology ID:
- D19-1173
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1630–1641
- Language:
- URL:
- https://aclanthology.org/D19-1173
- DOI:
- 10.18653/v1/D19-1173
- Cite (ACL):
- Nina Poerner and Hinrich Schütze. 2019. Multi-View Domain Adapted Sentence Embeddings for Low-Resource Unsupervised Duplicate Question Detection. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1630–1641, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Multi-View Domain Adapted Sentence Embeddings for Low-Resource Unsupervised Duplicate Question Detection (Poerner & Schütze, EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/D19-1173.pdf